/*=========================================================================
 *
 *  Copyright Insight Software Consortium
 *
 *  Licensed under the Apache License, Version 2.0 (the "License");
 *  you may not use this file except in compliance with the License.
 *  You may obtain a copy of the License at
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 *         http://www.apache.org/licenses/LICENSE-2.0.txt
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 *  distributed under the License is distributed on an "AS IS" BASIS,
 *  WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 *  See the License for the specific language governing permissions and
 *  limitations under the License.
 *
 *=========================================================================*/
/*=========================================================================
 *
 *  Portions of this file are subject to the VTK Toolkit Version 3 copyright.
 *
 *  Copyright (c) Ken Martin, Will Schroeder, Bill Lorensen
 *
 *  For complete copyright, license and disclaimer of warranty information
 *  please refer to the NOTICE file at the top of the ITK source tree.
 *
 *=========================================================================*/
#ifndef itkBSplineDecompositionImageFilter_hxx
#define itkBSplineDecompositionImageFilter_hxx
#include "itkBSplineDecompositionImageFilter.h"
#include "itkImageAlgorithm.h"
#include "itkProgressReporter.h"
#include "itkVector.h"
#include "itkPrintHelper.h"

namespace itk
{

template <typename TInputImage, typename TOutputImage>
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::BSplineDecompositionImageFilter()

{
  this->SetSplineOrder(3);

  for (unsigned int i = 0; i < m_Scratch.size(); ++i)
  {
    m_Scratch[i] = 0;
  }

  m_DataLength.Fill(itk::NumericTraits<typename TInputImage::SizeType::SizeValueType>::ZeroValue());
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::PrintSelf(std::ostream & os, Indent indent) const
{
  using namespace print_helper;

  Superclass::PrintSelf(os, indent);

  os << indent << "Scratch: " << m_Scratch << std::endl;
  os << indent << "Data Length: " << m_DataLength << std::endl;
  os << indent << "Spline Order: " << m_SplineOrder << std::endl;
  os << indent << "SplinePoles: " << m_SplinePoles << std::endl;
  os << indent << "Number Of Poles: " << m_NumberOfPoles << std::endl;
  os << indent << "Tolerance: " << m_Tolerance << std::endl;
  os << indent << "Iterator Direction: " << m_IteratorDirection << std::endl;
}

template <typename TInputImage, typename TOutputImage>
bool
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::DataToCoefficients1D()
{
  // See Unser, 1993, Part II, Equation 2.5,
  // or Unser, 1999, Box 2. for an explanation.

  double c0 = 1.0;

  if (m_DataLength[m_IteratorDirection] == 1) // Required by mirror boundaries
  {
    return false;
  }

  // Compute over all gain
  for (int k = 0; k < m_NumberOfPoles; k++)
  {
    // Note for cubic splines lambda = 6
    c0 = c0 * (1.0 - m_SplinePoles[k]) * (1.0 - 1.0 / m_SplinePoles[k]);
  }

  // Apply the gain
  for (unsigned int n = 0; n < m_DataLength[m_IteratorDirection]; n++)
  {
    m_Scratch[n] *= c0;
  }

  // Loop over all poles
  for (int k = 0; k < m_NumberOfPoles; k++)
  {
    // Causal initialization
    this->SetInitialCausalCoefficient(m_SplinePoles[k]);
    // Causal recursion
    for (unsigned int n = 1; n < m_DataLength[m_IteratorDirection]; n++)
    {
      m_Scratch[n] += m_SplinePoles[k] * m_Scratch[n - 1];
    }

    // anticausal initialization
    this->SetInitialAntiCausalCoefficient(m_SplinePoles[k]);
    // anticausal recursion
    for (int n = m_DataLength[m_IteratorDirection] - 2; 0 <= n; n--)
    {
      m_Scratch[n] = m_SplinePoles[k] * (m_Scratch[n + 1] - m_Scratch[n]);
    }
  }
  return true;
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetSplineOrder(unsigned int SplineOrder)
{
  if (SplineOrder == m_SplineOrder)
  {
    return;
  }
  m_SplinePoles.clear();
  m_SplineOrder = SplineOrder;
  this->SetPoles();
  this->Modified();
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetPoles()
{
  // See Unser, 1997. Part II, Table I for Pole values.
  // See also, Handbook of Medical Imaging, Processing and Analysis, Ed. Isaac
  // N. Bankman, 2000, pg. 416.
  switch (m_SplineOrder)
  {
    case 3:
      m_NumberOfPoles = 1;
      m_SplinePoles.resize(m_NumberOfPoles);
      m_SplinePoles.at(0) = std::sqrt(3.0) - 2.0;
      break;
    case 0:
      m_NumberOfPoles = 0;
      break;
    case 1:
      m_NumberOfPoles = 0;
      break;
    case 2:
      m_NumberOfPoles = 1;
      m_SplinePoles.resize(m_NumberOfPoles);
      m_SplinePoles.at(0) = std::sqrt(8.0) - 3.0;
      break;
    case 4:
      m_NumberOfPoles = 2;
      m_SplinePoles.resize(m_NumberOfPoles);
      m_SplinePoles.at(0) = std::sqrt(664.0 - std::sqrt(438976.0)) + std::sqrt(304.0) - 19.0;
      m_SplinePoles.at(1) = std::sqrt(664.0 + std::sqrt(438976.0)) - std::sqrt(304.0) - 19.0;
      break;
    case 5:
      m_NumberOfPoles = 2;
      m_SplinePoles.resize(m_NumberOfPoles);
      m_SplinePoles.at(0) = std::sqrt(135.0 / 2.0 - std::sqrt(17745.0 / 4.0)) + std::sqrt(105.0 / 4.0) - 13.0 / 2.0;
      m_SplinePoles.at(1) = std::sqrt(135.0 / 2.0 + std::sqrt(17745.0 / 4.0)) - std::sqrt(105.0 / 4.0) - 13.0 / 2.0;
      break;
    default:
      // SplineOrder not implemented yet.
      itkExceptionMacro(<< "SplineOrder must be between 0 and 5. Requested spline order has not been implemented yet.");
      break;
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetInitialCausalCoefficient(double z)
{
  // See Unser, 1999, Box 2 for explanation
  CoeffType                           sum;
  double                              zn, z2n, iz;
  typename TInputImage::SizeValueType horizon;

  // Yhis initialization corresponds to mirror boundaries
  horizon = m_DataLength[m_IteratorDirection];
  zn = z;
  if (m_Tolerance > 0.0)
  {
    horizon = (typename TInputImage::SizeValueType)std::ceil(std::log(m_Tolerance) / std::log(std::fabs(z)));
  }
  if (horizon < m_DataLength[m_IteratorDirection])
  {
    // Accelerated loop
    sum = m_Scratch[0]; // verify this
    for (unsigned int n = 1; n < horizon; n++)
    {
      sum += zn * m_Scratch[n];
      zn *= z;
    }
    m_Scratch[0] = sum;
  }
  else
  {
    // Full loop
    iz = 1.0 / z;
    z2n = std::pow(z, (double)(m_DataLength[m_IteratorDirection] - 1L));
    sum = m_Scratch[0] + z2n * m_Scratch[m_DataLength[m_IteratorDirection] - 1L];
    z2n *= z2n * iz;
    for (unsigned int n = 1; n <= (m_DataLength[m_IteratorDirection] - 2); n++)
    {
      sum += (zn + z2n) * m_Scratch[n];
      zn *= z;
      z2n *= iz;
    }
    m_Scratch[0] = sum / (1.0 - zn * zn);
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::SetInitialAntiCausalCoefficient(double z)
{
  // This initialization corresponds to mirror boundaries.
  // See Unser, 1999, Box 2 for explanation.
  // Also see erratum at http://bigwww.epfl.ch/publications/unser9902.html
  m_Scratch[m_DataLength[m_IteratorDirection] - 1] =
    (z / (z * z - 1.0)) *
    (z * m_Scratch[m_DataLength[m_IteratorDirection] - 2] + m_Scratch[m_DataLength[m_IteratorDirection] - 1]);
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::DataToCoefficientsND()
{
  OutputImagePointer output = this->GetOutput();

  Size<ImageDimension> size = output->GetBufferedRegion().GetSize();

  unsigned int count = output->GetBufferedRegion().GetNumberOfPixels() / size[0] * ImageDimension;

  ProgressReporter progress(this, 0, count, 10);

  // Initialize coeffient array
  this->CopyImageToImage(); // Coefficients are initialized to the input data

  // Loop through each dimension
  for (unsigned int n = 0; n < ImageDimension; n++)
  {
    m_IteratorDirection = n;

    // Initialize iterators
    OutputLinearIterator CIterator(output, output->GetBufferedRegion());
    CIterator.SetDirection(m_IteratorDirection);
    // For each data vector
    while (!CIterator.IsAtEnd())
    {
      // Copy coefficients to scratch
      this->CopyCoefficientsToScratch(CIterator);

      // Perform 1D BSpline calculations
      this->DataToCoefficients1D();

      // Copy scratch back to coefficients.
      // Brings us back to the end of the line we were working on.
      CIterator.GoToBeginOfLine();
      this->CopyScratchToCoefficients(CIterator); // m_Scratch = m_Image;
      CIterator.NextLine();
      progress.CompletedPixel();
    }
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyImageToImage()
{
  const TInputImage * const inputImage = this->GetInput();
  TOutputImage * const      outputImage = this->GetOutput();
  ImageAlgorithm::Copy(inputImage, outputImage, inputImage->GetBufferedRegion(), outputImage->GetBufferedRegion());
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyScratchToCoefficients(OutputLinearIterator & Iter)
{
  using OutputPixelType = typename TOutputImage::PixelType;
  typename TOutputImage::SizeValueType j = 0;
  while (!Iter.IsAtEndOfLine())
  {
    Iter.Set(static_cast<OutputPixelType>(m_Scratch[j]));
    ++Iter;
    ++j;
  }
}

/**
 */
template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::CopyCoefficientsToScratch(OutputLinearIterator & Iter)
{
  typename TOutputImage::SizeValueType j = 0;

  while (!Iter.IsAtEndOfLine())
  {
    m_Scratch[j] = static_cast<CoeffType>(Iter.Get());
    ++Iter;
    ++j;
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::GenerateInputRequestedRegion()
{
  // This filter requires all of the input image to be in the buffer
  InputImagePointer inputPtr = const_cast<TInputImage *>(this->GetInput());

  if (inputPtr)
  {
    inputPtr->SetRequestedRegionToLargestPossibleRegion();
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::EnlargeOutputRequestedRegion(DataObject * output)
{
  // This filter requires all of the input image to be in the buffer
  TOutputImage * imgData;

  imgData = dynamic_cast<TOutputImage *>(output);
  if (imgData)
  {
    imgData->SetRequestedRegionToLargestPossibleRegion();
  }
}

template <typename TInputImage, typename TOutputImage>
void
BSplineDecompositionImageFilter<TInputImage, TOutputImage>::GenerateData()
{
  // Allocate scratch memory
  InputImageConstPointer inputPtr = this->GetInput();

  m_DataLength = inputPtr->GetBufferedRegion().GetSize();

  typename TOutputImage::SizeValueType maxLength = 0;
  for (unsigned int n = 0; n < ImageDimension; n++)
  {
    if (m_DataLength[n] > maxLength)
    {
      maxLength = m_DataLength[n];
    }
  }
  m_Scratch.resize(maxLength);

  // Allocate memory for output image
  OutputImagePointer outputPtr = this->GetOutput();
  outputPtr->SetBufferedRegion(outputPtr->GetRequestedRegion());
  outputPtr->Allocate();

  // Calculate actual output
  this->DataToCoefficientsND();

  // Clean up
  m_Scratch.clear();
}
} // namespace itk

#endif
